device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load Data
train_dataset = datasets.MNIST(
root="../dataset/", train=True, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=128)
test_dataset = datasets.MNIST(
root="../dataset/", train=False, transform=transforms.ToTensor(), download=True
)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)
# Initialize network
model = CNN.CNN_2().to(device) # Choose network CNN_1 or CNN_2
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=momentum)
# Train Network
for epoch in range(n_epochs):
print("EPOCH", epoch)
for batch_idx, (data, targets) in enumerate(train_loader):
# get data to cuda if possible
data = data.to(device=device)
targets = targets.to(device=device)
# forward
